AI Consulting

This is a primer for delivering value in AI Operations.

Suggested Skills

• Extensive prompt engineering with Bing, Bard and GPT4 (including GPT plugins, sandbox and shots), prompt templating and chaining
• Microsoft Copilot will be significant with tight O365 integration, but is still in private beta with only 1k users worldwide
• LLaMA familiarity
• Midjourney/Stable Diffusion familiarity
• RPA (Robotic Process Automation) experience is a plus, eg: Pega
• Zapier or Make (integrations)
• No-code app experience (eg. Bubble, Retool, Notion, Airtable)
• Github Copilot (coding)
• Basic coding skills (Python, JavaScript) to understand and troubleshoot AI generated code
• Reengineering/first principles thinking mindset

Services

Up front services

• Chatbots
• Automations
• Custom data to train models
• Prompt libraries

Retainer services

• Monitoring & optimising costs
• Updating models & techniques
• Regular tests to track performance and address prompt drift
• Prompt engineering training
• Prompt engineering 1-on-1 assistance: to individual users for specific use cases.

Pricing is determined by

• Consultant expertise
• Customer company size/potential benefit

Finding AI Use Cases

Initially, don’t look for ideas. Look for problems/frustrations that:

• an intern could be trained to perform satisfactorily
• are performed regularly and consistently enough to warrant automation
• are text-heavy

Eg: categorise and draft email responses for review.

Prompts

• break down into sub-tasks where possible (chaining)
• diverse data and examples give better extrapolation
• keep a human in the loop (review every result or at least sample results)
• test for hallucination
• test for bias
• keep a log of prompts, settings and results
• monitor for prompt drift

Cloud vs On-prem

Cloud risks include:

• IP risk: 
• Regurgitation risk
• Information risk
• Inference risk

Sample Hardware Options